9 papers with code • 0 benchmarks • 1 datasets
In this paper, we consider the task of digitally voicing silent speech, where silently mouthed words are converted to audible speech based on electromyography (EMG) sensor measurements that capture muscle impulses.
This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for augmenting Parkinson’s Disease (PD) electromyography (EMG) signals.
To make up this, we introduce a new, large-scale EV-Action dataset in this work, which consists of RGB, depth, electromyography (EMG), and two skeleton modalities.
Ranked #4 on Multimodal Activity Recognition on EV-Action
The link between different psychophysiological measures during emotion episodes is not well understood.
State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues.
This paper proposes a comparison between different neural network models, using multilayer perceptron (MLPs) and recurrent neural network (RNN) models, for predicting Parkinson's disease electromyography (EMG) signals, to anticipate resulting resting tremor patterns.
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge.